RL starter files in order to immediately train, visualize and evaluate an agent without writing any line of code

Multi-hop knowledge graph reasoning learned via policy gradient with reward shaping and action dropout

Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO

This repo implements our paper, "Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt", which has been accepted at NeurIPS 2023.

Guide Your Agent with Adaptive Multimodal Rewards (NeurIPS 2023 Accepted)

TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets

Dota 2 bot that is trained by Deep RL with expert demonstrations

Code for NeurIPS 2022 paper Exploiting Reward Shifting in Value-Based Deep RL

Bayesian Reward Shaping Framework for Deep Reinforcement Learning

Code for "DrS: Learning Reusable Dense Rewards for Multi-Stage Tasks"

3D gym environments to train RL agents to play the Slime Volleyball game in 3 dimensions using Webots as simulator.

Benchmarks for risk-aware reward shaping of autonomous driving

Reward shaping library

Set of experiments of using weights of a previously trained network as prior knowledge for a more complicated one and reward providing.

A gymnasium-compatible framework to create reinforcement learning (RL) environment for solving the optimal power flow (OPF) problem. Contains five OPF benchmark environments for comparable research.

A lightweight package for running small experiments with reward shaping in reinforcement learning.

Reinforcement Learning Exploration of PPO and training methods in Rocket League

Prepares policies from data to model; focuses on hierarchical tasks and applies reward shaping to handle delayed reward signals.

Code from the IJCAI 2019 paper "Controllable Neural Story Plot Generation via Reward Shaping"